class_docstr: Allows a chat bot to be trained using a list of strings
  where the list represents a conversation.
docstr: Train the chat bot based on the provided list of
  statements that represents a single conversation.
docstr_tok: ["Train","the","chat","bot","based","on","the","provided","list","of","statements","that","represents","a","single","conversation","."]
code: def train(self, conversation):
      """
      Train the chat bot based on the provided list of
      statements that represents a single conversation.
      """
      previous_statement_text = None
      previous_statement_search_text = ""
  
      statements_to_create = []
  
      for conversation_count, text in enumerate(conversation):
          if self.show_training_progress:
              utils.print_progress_bar(
                  "List Trainer",
                  conversation_count + 1,
                  len(conversation),
              )
  
          statement_search_text = (
              self.chatbot.storage.tagger.get_text_index_string(text)
          )
  
          statement = self.get_preprocessed_statement(
              Statement(
                  text=text,
                  search_text=statement_search_text,
                  in_response_to=previous_statement_text,
                  search_in_response_to=previous_statement_search_text,
                  conversation="training",
              )
          )
  
          previous_statement_text = statement.text
          previous_statement_search_text = statement_search_text
  
          statements_to_create.append(statement)
  
      self.chatbot.storage.create_many(statements_to_create)
  
imports: import os
  import sys
  import csv
  import time
  from dateutil import parser
  from chatterbot.conversation import Statement
  from chatterbot.tagging import PosLemmaTagger
  from chatterbot import utils
label_desc: Use image node_id 1 for calling the ListTrainer obj's underlying member method code with example usage: obj.train(conversation) without return types